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Python ProbabilisticDirectionGetter.from_pmf方法代码示例

本文整理汇总了Python中dipy.direction.ProbabilisticDirectionGetter.from_pmf方法的典型用法代码示例。如果您正苦于以下问题:Python ProbabilisticDirectionGetter.from_pmf方法的具体用法?Python ProbabilisticDirectionGetter.from_pmf怎么用?Python ProbabilisticDirectionGetter.from_pmf使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在dipy.direction.ProbabilisticDirectionGetter的用法示例。


在下文中一共展示了ProbabilisticDirectionGetter.from_pmf方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_ProbabilisticDirectionGetter

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
def test_ProbabilisticDirectionGetter():
    # Test the constructors and errors of the ProbabilisticDirectionGetter

    class SillyModel(SphHarmModel):

        sh_order = 4

        def fit(self, data, mask=None):
            coeff = np.zeros(data.shape[:-1] + (15,))
            return SphHarmFit(self, coeff, mask=None)

    model = SillyModel(gtab=None)
    data = np.zeros((3, 3, 3, 7))

    # Test if the tracking works on different dtype of the same data.
    for dtype in [np.float32, np.float64]:
        fit = model.fit(data.astype(dtype))

        # Sample point and direction
        point = np.zeros(3)
        dir = unit_octahedron.vertices[0].copy()

        # make a dg from a fit
        dg = ProbabilisticDirectionGetter.from_shcoeff(fit.shm_coeff, 90,
                                                       unit_octahedron)
        state = dg.get_direction(point, dir)
        npt.assert_equal(state, 1)

        # Make a dg from a pmf
        N = unit_octahedron.theta.shape[0]
        pmf = np.zeros((3, 3, 3, N))
        dg = ProbabilisticDirectionGetter.from_pmf(pmf, 90, unit_octahedron)
        state = dg.get_direction(point, dir)
        npt.assert_equal(state, 1)

        # pmf shape must match sphere
        bad_pmf = pmf[..., 1:]
        npt.assert_raises(ValueError, ProbabilisticDirectionGetter.from_pmf,
                          bad_pmf, 90, unit_octahedron)

        # pmf must have 4 dimensions
        bad_pmf = pmf[0, ...]
        npt.assert_raises(ValueError, ProbabilisticDirectionGetter.from_pmf,
                          bad_pmf, 90, unit_octahedron)
        # pmf cannot have negative values
        pmf[0, 0, 0, 0] = -1
        npt.assert_raises(ValueError, ProbabilisticDirectionGetter.from_pmf,
                          pmf, 90, unit_octahedron)

        # Check basis_type keyword
        dg = ProbabilisticDirectionGetter.from_shcoeff(fit.shm_coeff, 90,
                                                       unit_octahedron,
                                                       basis_type="mrtrix")

        npt.assert_raises(ValueError,
                          ProbabilisticDirectionGetter.from_shcoeff,
                          fit.shm_coeff, 90, unit_octahedron,
                          basis_type="not a basis")
开发者ID:StongeEtienne,项目名称:dipy,代码行数:60,代码来源:test_prob_direction_getter.py

示例2: LocalTracking

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
distribution of small fiber bundles within each voxel. We can use this
distribution for probabilistic fiber tracking. One way to do this is to
represent the FOD using a discrete sphere. This discrete FOD can be used by the
Probabilistic Direction Getter as a PMF for sampling tracking directions. We
need to clip the FOD to use it as a PMF because the latter cannot have negative
values. (Ideally the FOD should be strictly positive, but because of noise
and/or model failures sometimes it can have negative values).
"""

from dipy.direction import ProbabilisticDirectionGetter
from dipy.data import small_sphere
from dipy.io.trackvis import save_trk

fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf, max_angle=30.,
                                                sphere=small_sphere)
streamlines = LocalTracking(prob_dg, classifier, seeds, affine, step_size=.5)
save_trk("probabilistic_small_sphere.trk", streamlines, affine, labels.shape)

"""
One disadvantage of using a discrete PMF to represent possible tracking
directions is that it tends to take up a lot of memory (RAM). The size of the
PMF, the FOD in this case, must be equal to the number of possible tracking
directions on the hemisphere, and every voxel has a unique PMF. In this case
the data is ``(81, 106, 76)`` and ``small_sphere`` has 181 directions so the
FOD is ``(81, 106, 76, 181)``. One way to avoid sampling the PMF and holding it
in memory is to build the direction getter directly from the spherical harmonic
representation of the FOD. By using this approach, we can also use a larger
sphere, like ``default_sphere`` which has 362 directions on the hemisphere,
without having to worry about memory limitations.
"""
开发者ID:MPDean,项目名称:dipy,代码行数:34,代码来源:probabilistic_fiber_tracking.py

示例3: test_particle_filtering_tractography

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
def test_particle_filtering_tractography():
    """This tests that the ParticleFilteringTracking produces
    more streamlines connecting the gray matter than LocalTracking.
    """
    sphere = get_sphere('repulsion100')
    step_size = 0.2

    # Simple tissue masks
    simple_wm = np.array([[0, 0, 0, 0, 0, 0],
                          [0, 0, 1, 0, 0, 0],
                          [0, 1, 1, 1, 0, 0],
                          [0, 1, 1, 1, 0, 0],
                          [0, 0, 0, 0, 0, 0]])
    simple_wm = np.dstack([np.zeros(simple_wm.shape),
                           simple_wm,
                           simple_wm,
                           simple_wm,
                           np.zeros(simple_wm.shape)])
    simple_gm = np.array([[1, 1, 0, 0, 0, 0],
                          [1, 1, 0, 0, 0, 0],
                          [0, 1, 0, 0, 1, 0],
                          [0, 0, 0, 0, 1, 0],
                          [0, 0, 0, 0, 0, 0]])
    simple_gm = np.dstack([np.zeros(simple_gm.shape),
                           simple_gm,
                           simple_gm,
                           simple_gm,
                           np.zeros(simple_gm.shape)])
    simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
    tc = ActTissueClassifier.from_pve(simple_wm, simple_gm, simple_csf)
    seeds = seeds_from_mask(simple_wm, density=2)

    # Random pmf in every voxel
    shape_img = list(simple_wm.shape)
    shape_img.extend([sphere.vertices.shape[0]])
    np.random.seed(0)  # Random number generator initialization
    pmf = np.random.random(shape_img)

    # Test that PFT recover equal or more streamlines than localTracking
    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 60, sphere)
    local_streamlines_generator = LocalTracking(dg, tc, seeds, np.eye(4),
                                                step_size, max_cross=1,
                                                return_all=False)
    local_streamlines = Streamlines(local_streamlines_generator)

    pft_streamlines_generator = ParticleFilteringTracking(
        dg, tc, seeds, np.eye(4), step_size, max_cross=1, return_all=False,
        pft_back_tracking_dist=1, pft_front_tracking_dist=0.5)
    pft_streamlines = Streamlines(pft_streamlines_generator)

    npt.assert_(np.array([len(pft_streamlines) > 0]))
    npt.assert_(np.array([len(pft_streamlines) >= len(local_streamlines)]))

    # Test that all points are equally spaced
    for l in [1, 2, 5, 10, 100]:
        pft_streamlines = ParticleFilteringTracking(dg, tc, seeds, np.eye(4),
                                                    step_size, max_cross=1,
                                                    return_all=True, maxlen=l)
        for s in pft_streamlines:
            for i in range(len(s) - 1):
                npt.assert_almost_equal(np.linalg.norm(s[i] - s[i + 1]),
                                        step_size)
    # Test that all points are within the image volume
    seeds = seeds_from_mask(np.ones(simple_wm.shape), density=1)
    pft_streamlines_generator = ParticleFilteringTracking(
        dg, tc, seeds, np.eye(4), step_size, max_cross=1, return_all=True)
    pft_streamlines = Streamlines(pft_streamlines_generator)

    for s in pft_streamlines:
        npt.assert_(np.all((s + 0.5).astype(int) >= 0))
        npt.assert_(np.all((s + 0.5).astype(int) < simple_wm.shape))

    # Test that the number of streamline return with return_all=True equal the
    # number of seeds places
    npt.assert_(np.array([len(pft_streamlines) == len(seeds)]))

    # Test non WM seed position
    seeds = [[0, 5, 4], [0, 0, 1], [50, 50, 50]]
    pft_streamlines_generator = ParticleFilteringTracking(
        dg, tc, seeds, np.eye(4), step_size, max_cross=1, return_all=True)
    pft_streamlines = Streamlines(pft_streamlines_generator)

    npt.assert_equal(len(pft_streamlines[0]), 3)  # INVALIDPOINT
    npt.assert_equal(len(pft_streamlines[1]), 3)  # ENDPOINT
    npt.assert_equal(len(pft_streamlines[2]), 1)  # OUTSIDEIMAGE

    # Test with wrong tissueclassifier type
    tc_bin = BinaryTissueClassifier(simple_wm)
    npt.assert_raises(ValueError,
                      lambda: ParticleFilteringTracking(dg, tc_bin, seeds,
                                                        np.eye(4), step_size))
    # Test with invalid back/front tracking distances
    npt.assert_raises(
        ValueError,
        lambda: ParticleFilteringTracking(dg, tc, seeds, np.eye(4), step_size,
                                          pft_back_tracking_dist=0,
                                          pft_front_tracking_dist=0))
    npt.assert_raises(
        ValueError,
        lambda: ParticleFilteringTracking(dg, tc, seeds, np.eye(4), step_size,
#.........这里部分代码省略.........
开发者ID:MarcCote,项目名称:dipy,代码行数:103,代码来源:test_tracking.py

示例4: test_stop_conditions

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
def test_stop_conditions():
    """This tests that the Local Tracker behaves as expected for the
    following tissue types.
    """
    # TissueTypes.TRACKPOINT = 1
    # TissueTypes.ENDPOINT = 2
    # TissueTypes.INVALIDPOINT = 0
    tissue = np.array([[2, 1, 1, 2, 1],
                       [2, 2, 1, 1, 2],
                       [1, 1, 1, 1, 1],
                       [1, 1, 1, 2, 2],
                       [0, 1, 1, 1, 2],
                       [0, 1, 1, 0, 2],
                       [1, 0, 1, 1, 1]])
    tissue = tissue[None]

    sphere = HemiSphere.from_sphere(unit_octahedron)
    pmf_lookup = np.array([[0., 0., 0., ],
                           [0., 0., 1.]])
    pmf = pmf_lookup[(tissue > 0).astype("int")]

    # Create a seeds along
    x = np.array([0., 0, 0, 0, 0, 0, 0])
    y = np.array([0., 1, 2, 3, 4, 5, 6])
    z = np.array([1., 1, 1, 0, 1, 1, 1])
    seeds = np.column_stack([x, y, z])

    # Set up tracking
    endpoint_mask = tissue == TissueTypes.ENDPOINT
    invalidpoint_mask = tissue == TissueTypes.INVALIDPOINT
    tc = ActTissueClassifier(endpoint_mask, invalidpoint_mask)
    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 60, sphere)

    # valid streamlines only
    streamlines_generator = LocalTracking(direction_getter=dg,
                                          tissue_classifier=tc,
                                          seeds=seeds,
                                          affine=np.eye(4),
                                          step_size=1.,
                                          return_all=False)
    streamlines_not_all = iter(streamlines_generator)

    # all streamlines
    streamlines_all_generator = LocalTracking(direction_getter=dg,
                                              tissue_classifier=tc,
                                              seeds=seeds,
                                              affine=np.eye(4),
                                              step_size=1.,
                                              return_all=True)
    streamlines_all = iter(streamlines_all_generator)

    # Check that the first streamline stops at 0 and 3 (ENDPOINT)
    y = 0
    sl = next(streamlines_not_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 3])
    npt.assert_equal(len(sl), 4)

    sl = next(streamlines_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 3])
    npt.assert_equal(len(sl), 4)

    # Check that the first streamline stops at 0 and 4 (ENDPOINT)
    y = 1
    sl = next(streamlines_not_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 4])
    npt.assert_equal(len(sl), 5)

    sl = next(streamlines_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 4])
    npt.assert_equal(len(sl), 5)

    # This streamline should be the same as above. This row does not have
    # ENDPOINTs, but the streamline should stop at the edge and not include
    # OUTSIDEIMAGE points.
    y = 2
    sl = next(streamlines_not_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 4])
    npt.assert_equal(len(sl), 5)

    sl = next(streamlines_all)
    npt.assert_equal(sl[0], [0, y, 0])
    npt.assert_equal(sl[-1], [0, y, 4])
    npt.assert_equal(len(sl), 5)

    # If we seed on the edge, the first (or last) point in the streamline
    # should be the seed.
    y = 3
    sl = next(streamlines_not_all)
    npt.assert_equal(sl[0], seeds[y])

    sl = next(streamlines_all)
    npt.assert_equal(sl[0], seeds[y])

    # The last 3 seeds should not produce streamlines,
    # INVALIDPOINT streamlines are rejected (return_all=False).
#.........这里部分代码省略.........
开发者ID:MarcCote,项目名称:dipy,代码行数:103,代码来源:test_tracking.py

示例5: test_probabilistic_odf_weighted_tracker

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
def test_probabilistic_odf_weighted_tracker():
    """This tests that the Probabalistic Direction Getter plays nice
    LocalTracking and produces reasonable streamlines in a simple example.
    """
    sphere = HemiSphere.from_sphere(unit_octahedron)

    # A simple image with three possible configurations, a vertical tract,
    # a horizontal tract and a crossing
    pmf_lookup = np.array([[0., 0., 1.],
                           [1., 0., 0.],
                           [0., 1., 0.],
                           [.6, .4, 0.]])
    simple_image = np.array([[0, 1, 0, 0, 0, 0],
                             [0, 1, 0, 0, 0, 0],
                             [0, 3, 2, 2, 2, 0],
                             [0, 1, 0, 0, 0, 0],
                             [0, 1, 0, 0, 0, 0],
                             ])

    simple_image = simple_image[..., None]
    pmf = pmf_lookup[simple_image]

    seeds = [np.array([1., 1., 0.])] * 30

    mask = (simple_image > 0).astype(float)
    tc = ThresholdTissueClassifier(mask, .5)

    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 90, sphere,
                                               pmf_threshold=0.1)
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    expected = [np.array([[0., 1., 0.],
                          [1., 1., 0.],
                          [2., 1., 0.],
                          [2., 2., 0.],
                          [2., 3., 0.],
                          [2., 4., 0.],
                          [2., 5., 0.]]),
                np.array([[0., 1., 0.],
                          [1., 1., 0.],
                          [2., 1., 0.],
                          [3., 1., 0.],
                          [4., 1., 0.]])]

    def allclose(x, y):
        return x.shape == y.shape and np.allclose(x, y)

    path = [False, False]
    for sl in streamlines:
        if allclose(sl, expected[0]):
            path[0] = True
        elif allclose(sl, expected[1]):
            path[1] = True
        else:
            raise AssertionError()
    npt.assert_(all(path))

    # The first path is not possible if 90 degree turns are excluded
    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 80, sphere,
                                               pmf_threshold=0.1)
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    for sl in streamlines:
        npt.assert_(np.allclose(sl, expected[1]))

    # The first path is not possible if pmf_threshold > 0.67
    # 0.4/0.6 < 2/3, multiplying the pmf should not change the ratio
    dg = ProbabilisticDirectionGetter.from_pmf(10*pmf, 90, sphere,
                                               pmf_threshold=0.67)
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    for sl in streamlines:
        npt.assert_(np.allclose(sl, expected[1]))

    # Test non WM seed position
    seeds = [[0, 0, 0], [5, 5, 5]]
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 0.2, max_cross=1,
                                return_all=True)
    streamlines = Streamlines(streamlines)
    npt.assert_(len(streamlines[0]) == 3)  # INVALIDPOINT
    npt.assert_(len(streamlines[1]) == 1)  # OUTSIDEIMAGE

    # Test that all points are within the image volume
    seeds = seeds_from_mask(np.ones(mask.shape), density=2)
    streamline_generator = LocalTracking(dg, tc, seeds, np.eye(4), 0.5,
                                         return_all=True)
    streamlines = Streamlines(streamline_generator)
    for s in streamlines:
        npt.assert_(np.all((s + 0.5).astype(int) >= 0))
        npt.assert_(np.all((s + 0.5).astype(int) < mask.shape))
    # Test that the number of streamline return with return_all=True equal the
    # number of seeds places

    npt.assert_(np.array([len(streamlines) == len(seeds)]))

    # Test reproducibility
    tracking_1 = Streamlines(LocalTracking(dg, tc, seeds, np.eye(4),
                                           0.5,
                                           random_seed=0)).data
    tracking_2 = Streamlines(LocalTracking(dg, tc, seeds, np.eye(4),
#.........这里部分代码省略.........
开发者ID:MarcCote,项目名称:dipy,代码行数:103,代码来源:test_tracking.py

示例6: test_ProbabilisticOdfWeightedTracker

# 需要导入模块: from dipy.direction import ProbabilisticDirectionGetter [as 别名]
# 或者: from dipy.direction.ProbabilisticDirectionGetter import from_pmf [as 别名]
def test_ProbabilisticOdfWeightedTracker():
    """This tests that the Probabalistic Direction Getter plays nice
    LocalTracking and produces reasonable streamlines in a simple example.
    """
    sphere = HemiSphere.from_sphere(unit_octahedron)

    # A simple image with three possible configurations, a vertical tract,
    # a horizontal tract and a crossing
    pmf_lookup = np.array([[0., 0., 1.],
                           [1., 0., 0.],
                           [0., 1., 0.],
                           [.5, .5, 0.]])
    simple_image = np.array([[0, 1, 0, 0, 0, 0],
                             [0, 1, 0, 0, 0, 0],
                             [0, 3, 2, 2, 2, 0],
                             [0, 1, 0, 0, 0, 0],
                             [0, 1, 0, 0, 0, 0],
                             ])

    simple_image = simple_image[..., None]
    pmf = pmf_lookup[simple_image]

    seeds = [np.array([1., 1., 0.])] * 30

    mask = (simple_image > 0).astype(float)
    tc = ThresholdTissueClassifier(mask, .5)

    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 90, sphere)
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    expected = [np.array([[ 0.,  1.,  0.],
                          [ 1.,  1.,  0.],
                          [ 2.,  1.,  0.],
                          [ 2.,  2.,  0.],
                          [ 2.,  3.,  0.],
                          [ 2.,  4.,  0.],
                          [ 2.,  5.,  0.]]),
                np.array([[ 0.,  1.,  0.],
                          [ 1.,  1.,  0.],
                          [ 2.,  1.,  0.],
                          [ 3.,  1.,  0.],
                          [ 4.,  1.,  0.]])
               ]

    def allclose(x, y):
        return x.shape == y.shape and np.allclose(x, y)

    path = [False, False]
    for sl in streamlines:
        dir = ( -sphere.vertices[0] ).copy()
        if allclose(sl, expected[0]):
            path[0] = True
        elif allclose(sl, expected[1]):
            path[1] = True
        else:
            raise AssertionError()
    npt.assert_(all(path))

    # The first path is not possible if 90 degree turns are excluded
    dg = ProbabilisticDirectionGetter.from_pmf(pmf, 80, sphere)
    streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)

    for sl in streamlines:
        npt.assert_(np.allclose(sl, expected[1]))
开发者ID:Paolopost,项目名称:dipy,代码行数:66,代码来源:test_local_tracking.py


注:本文中的dipy.direction.ProbabilisticDirectionGetter.from_pmf方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。